Overview

Dataset statistics

Number of variables14
Number of observations37307
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 MiB
Average record size in memory112.0 B

Variable types

Numeric12
Unsupported1
Categorical1

Warnings

views is highly correlated with likes and 1 other fieldsHigh correlation
likes is highly correlated with views and 2 other fieldsHigh correlation
dislikes is highly correlated with likes and 1 other fieldsHigh correlation
comment_count is highly correlated with views and 2 other fieldsHigh correlation
views is highly correlated with likes and 2 other fieldsHigh correlation
likes is highly correlated with views and 2 other fieldsHigh correlation
dislikes is highly correlated with views and 2 other fieldsHigh correlation
comment_count is highly correlated with views and 2 other fieldsHigh correlation
Ratio_View_likes is highly correlated with Ratio_views_comment_count and 1 other fieldsHigh correlation
Ratio_View_dislikes is highly correlated with Ratio_likes_dislikesHigh correlation
Ratio_views_comment_count is highly correlated with Ratio_View_likesHigh correlation
Ratio_likes_dislikes is highly correlated with Ratio_View_likes and 1 other fieldsHigh correlation
views is highly correlated with likes and 2 other fieldsHigh correlation
likes is highly correlated with views and 2 other fieldsHigh correlation
dislikes is highly correlated with views and 2 other fieldsHigh correlation
comment_count is highly correlated with views and 2 other fieldsHigh correlation
Ratio_View_likes is highly correlated with Ratio_View_dislikesHigh correlation
Ratio_View_dislikes is highly correlated with Ratio_View_likesHigh correlation
views is highly correlated with comment_count and 1 other fieldsHigh correlation
comment_count is highly correlated with views and 2 other fieldsHigh correlation
likes is highly correlated with views and 2 other fieldsHigh correlation
dislikes is highly correlated with comment_count and 1 other fieldsHigh correlation
dislikes is highly skewed (γ1 = 28.43545183) Skewed
Ratio_View_likes is highly skewed (γ1 = 54.7018187) Skewed
Ratio_views_comment_count is highly skewed (γ1 = 74.25807043) Skewed
df_index has unique values Unique
publish_time_1 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2021-09-02 10:38:57.361995
Analysis finished2021-09-02 10:40:13.405337
Duration1 minute and 16.04 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct37307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19382.94486
Minimum0
Maximum38915
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:13.499742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1920.3
Q19707.5
median19362
Q329002
95-th percentile36983.7
Maximum38915
Range38915
Interquartile range (IQR)19294.5

Descriptive statistics

Standard deviation11209.38901
Coefficient of variation (CV)0.5783119692
Kurtosis-1.188597382
Mean19382.94486
Median Absolute Deviation (MAD)9648
Skewness0.006894843904
Sum723119524
Variance125650402
MonotonicityStrictly increasing
2021-09-02T16:10:13.627922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
257611
 
< 0.1%
257811
 
< 0.1%
257821
 
< 0.1%
257831
 
< 0.1%
257841
 
< 0.1%
257851
 
< 0.1%
257861
 
< 0.1%
257871
 
< 0.1%
257881
 
< 0.1%
Other values (37297)37297
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
389151
< 0.1%
389141
< 0.1%
389131
< 0.1%
389121
< 0.1%
389111
< 0.1%
389101
< 0.1%
389091
< 0.1%
389081
< 0.1%
389071
< 0.1%
389061
< 0.1%

category_id
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.73945908
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:13.743709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median20
Q324
95-th percentile26
Maximum43
Range42
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.724900544
Coefficient of variation (CV)0.4614785045
Kurtosis-1.095539639
Mean16.73945908
Median Absolute Deviation (MAD)6
Skewness-0.3524889223
Sum624499
Variance59.67408841
MonotonicityNot monotonic
2021-09-02T16:10:13.827837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1013537
36.3%
248626
23.1%
222738
 
7.3%
12408
 
6.5%
261921
 
5.1%
231794
 
4.8%
171784
 
4.8%
201672
 
4.5%
251124
 
3.0%
15527
 
1.4%
Other values (6)1176
 
3.2%
ValueCountFrequency (%)
12408
 
6.5%
2136
 
0.4%
1013537
36.3%
15527
 
1.4%
171784
 
4.8%
1989
 
0.2%
201672
 
4.5%
222738
 
7.3%
231794
 
4.8%
248626
23.1%
ValueCountFrequency (%)
4320
 
0.1%
2938
 
0.1%
28444
 
1.2%
27449
 
1.2%
261921
 
5.1%
251124
 
3.0%
248626
23.1%
231794
 
4.8%
222738
 
7.3%
201672
 
4.5%

views
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36984
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6004065.957
Minimum1014
Maximum424538912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:13.937411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1014
5-th percentile48527.1
Q1255845
median995938
Q33750771
95-th percentile24757060.4
Maximum424538912
Range424537898
Interquartile range (IQR)3494926

Descriptive statistics

Standard deviation19270692.81
Coefficient of variation (CV)3.209607113
Kurtosis113.42635
Mean6004065.957
Median Absolute Deviation (MAD)891506
Skewness9.00864677
Sum2.239936887 × 1011
Variance3.713596012 × 1014
MonotonicityNot monotonic
2021-09-02T16:10:14.055883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1913215
 
< 0.1%
185063
 
< 0.1%
1913273
 
< 0.1%
460593
 
< 0.1%
536852
 
< 0.1%
834482
 
< 0.1%
421502
 
< 0.1%
483082
 
< 0.1%
34905612
 
< 0.1%
6306142
 
< 0.1%
Other values (36974)37281
99.9%
ValueCountFrequency (%)
10141
< 0.1%
15051
< 0.1%
15401
< 0.1%
15591
< 0.1%
15661
< 0.1%
15711
< 0.1%
15771
< 0.1%
15811
< 0.1%
15831
< 0.1%
18581
< 0.1%
ValueCountFrequency (%)
4245389121
< 0.1%
4135866991
< 0.1%
4026508041
< 0.1%
3920368781
< 0.1%
3824014971
< 0.1%
3723993381
< 0.1%
3621115551
< 0.1%
3499871761
< 0.1%
3396294891
< 0.1%
3376215711
< 0.1%

likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29986
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138137.6737
Minimum5
Maximum5613827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:14.173407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile885
Q16362
median26359
Q3119267.5
95-th percentile578678
Maximum5613827
Range5613822
Interquartile range (IQR)112905.5

Descriptive statistics

Standard deviation354840.2362
Coefficient of variation (CV)2.56874339
Kurtosis64.96627895
Mean138137.6737
Median Absolute Deviation (MAD)24282
Skewness6.790122609
Sum5153502193
Variance1.259115933 × 1011
MonotonicityNot monotonic
2021-09-02T16:10:14.286501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3515
 
< 0.1%
40313
 
< 0.1%
2212
 
< 0.1%
5311
 
< 0.1%
32110
 
< 0.1%
30569
 
< 0.1%
3339
 
< 0.1%
8289
 
< 0.1%
5259
 
< 0.1%
2909
 
< 0.1%
Other values (29976)37201
99.7%
ValueCountFrequency (%)
53
 
< 0.1%
131
 
< 0.1%
171
 
< 0.1%
183
 
< 0.1%
192
 
< 0.1%
205
< 0.1%
2212
< 0.1%
294
 
< 0.1%
305
< 0.1%
314
 
< 0.1%
ValueCountFrequency (%)
56138271
< 0.1%
55952031
< 0.1%
55305681
< 0.1%
54863491
< 0.1%
54445411
< 0.1%
54390151
< 0.1%
54262741
< 0.1%
53869591
< 0.1%
53661501
< 0.1%
53291611
< 0.1%

dislikes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct10919
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7192.118342
Minimum1
Maximum1753274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:14.405141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q1213
median842
Q33422
95-th percentile27423.5
Maximum1753274
Range1753273
Interquartile range (IQR)3209

Descriptive statistics

Standard deviation41230.31061
Coefficient of variation (CV)5.732707479
Kurtosis1072.178273
Mean7192.118342
Median Absolute Deviation (MAD)762
Skewness28.43545183
Sum268316359
Variance1699938513
MonotonicityNot monotonic
2021-09-02T16:10:14.527875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15104
 
0.3%
2784
 
0.2%
2282
 
0.2%
1779
 
0.2%
1177
 
0.2%
7977
 
0.2%
7776
 
0.2%
1276
 
0.2%
376
 
0.2%
4475
 
0.2%
Other values (10909)36501
97.8%
ValueCountFrequency (%)
134
0.1%
266
0.2%
376
0.2%
446
0.1%
515
 
< 0.1%
631
0.1%
759
0.2%
864
0.2%
933
0.1%
1048
0.1%
ValueCountFrequency (%)
17532741
< 0.1%
17395791
< 0.1%
17328591
< 0.1%
17278261
< 0.1%
17223071
< 0.1%
17122841
< 0.1%
17048611
< 0.1%
16949451
< 0.1%
16833211
< 0.1%
16684601
< 0.1%

comment_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15654
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12976.33608
Minimum1
Maximum1228655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:14.656458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile137.3
Q1759.5
median2653
Q39589.5
95-th percentile51506.1
Maximum1228655
Range1228654
Interquartile range (IQR)8830

Descriptive statistics

Standard deviation44236.03692
Coefficient of variation (CV)3.408977438
Kurtosis240.1136368
Mean12976.33608
Median Absolute Deviation (MAD)2307
Skewness12.71974664
Sum484108170
Variance1956826962
MonotonicityNot monotonic
2021-09-02T16:10:14.783800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20143
 
0.1%
5039
 
0.1%
4038
 
0.1%
5336
 
0.1%
3734
 
0.1%
3033
 
0.1%
25730
 
0.1%
34629
 
0.1%
27128
 
0.1%
31628
 
0.1%
Other values (15644)36969
99.1%
ValueCountFrequency (%)
13
 
< 0.1%
22
 
< 0.1%
37
 
< 0.1%
417
< 0.1%
59
 
< 0.1%
68
 
< 0.1%
728
0.1%
82
 
< 0.1%
99
 
< 0.1%
1012
< 0.1%
ValueCountFrequency (%)
12286551
< 0.1%
12253261
< 0.1%
12131721
< 0.1%
12048671
< 0.1%
11971301
< 0.1%
11894561
< 0.1%
11653501
< 0.1%
11639771
< 0.1%
11422691
< 0.1%
11148001
< 0.1%

publish_time_1
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size291.6 KiB

publish_weekday
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size291.6 KiB
Friday
7407 
Wednesday
7088 
Thursday
6886 
Tuesday
5622 
Monday
5614 
Other values (2)
4690 

Length

Max length9
Median length7
Mean length7.2012759
Min length6

Characters and Unicode

Total characters268658
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowSunday
3rd rowFriday
4th rowMonday
5th rowMonday

Common Values

ValueCountFrequency (%)
Friday7407
19.9%
Wednesday7088
19.0%
Thursday6886
18.5%
Tuesday5622
15.1%
Monday5614
15.0%
Sunday2611
 
7.0%
Saturday2079
 
5.6%

Length

2021-09-02T16:10:15.213305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-02T16:10:15.288425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
friday7407
19.9%
wednesday7088
19.0%
thursday6886
18.5%
tuesday5622
15.1%
monday5614
15.0%
sunday2611
 
7.0%
saturday2079
 
5.6%

Most occurring characters

ValueCountFrequency (%)
d44395
16.5%
a39386
14.7%
y37307
13.9%
e19798
7.4%
s19596
7.3%
u17198
 
6.4%
r16372
 
6.1%
n15313
 
5.7%
T12508
 
4.7%
F7407
 
2.8%
Other values (7)39378
14.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter231351
86.1%
Uppercase Letter37307
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d44395
19.2%
a39386
17.0%
y37307
16.1%
e19798
8.6%
s19596
8.5%
u17198
 
7.4%
r16372
 
7.1%
n15313
 
6.6%
i7407
 
3.2%
h6886
 
3.0%
Other values (2)7693
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
T12508
33.5%
F7407
19.9%
W7088
19.0%
M5614
15.0%
S4690
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Latin268658
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d44395
16.5%
a39386
14.7%
y37307
13.9%
e19798
7.4%
s19596
7.3%
u17198
 
6.4%
r16372
 
6.1%
n15313
 
5.7%
T12508
 
4.7%
F7407
 
2.8%
Other values (7)39378
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII268658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d44395
16.5%
a39386
14.7%
y37307
13.9%
e19798
7.4%
s19596
7.3%
u17198
 
6.4%
r16372
 
6.1%
n15313
 
5.7%
T12508
 
4.7%
F7407
 
2.8%
Other values (7)39378
14.7%

title_length
Real number (ℝ≥0)

Distinct94
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.66486182
Minimum7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:15.401301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile21
Q134
median48
Q362
95-th percentile87
Maximum100
Range93
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.7604799
Coefficient of variation (CV)0.3978764699
Kurtosis-0.4201283405
Mean49.66486182
Median Absolute Deviation (MAD)14
Skewness0.4497771263
Sum1852847
Variance390.4765659
MonotonicityNot monotonic
2021-09-02T16:10:15.522881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34887
 
2.4%
36855
 
2.3%
33852
 
2.3%
49849
 
2.3%
30848
 
2.3%
51810
 
2.2%
42793
 
2.1%
35785
 
2.1%
48768
 
2.1%
43750
 
2.0%
Other values (84)29110
78.0%
ValueCountFrequency (%)
74
 
< 0.1%
86
 
< 0.1%
919
 
0.1%
1040
 
0.1%
1142
 
0.1%
1226
 
0.1%
13168
0.5%
14139
0.4%
15143
0.4%
1682
0.2%
ValueCountFrequency (%)
10053
 
0.1%
99132
0.4%
98136
0.4%
97167
0.4%
9686
0.2%
95201
0.5%
94170
0.5%
93146
0.4%
9291
0.2%
91123
0.3%

description_length
Real number (ℝ≥0)

Distinct1797
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean905.0065135
Minimum1
Maximum5260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:15.641451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile92
Q1356
median655
Q31229
95-th percentile2535
Maximum5260
Range5259
Interquartile range (IQR)873

Descriptive statistics

Standard deviation809.0663016
Coefficient of variation (CV)0.8939894791
Kurtosis4.292087135
Mean905.0065135
Median Absolute Deviation (MAD)379
Skewness1.849870025
Sum33763078
Variance654588.2804
MonotonicityNot monotonic
2021-09-02T16:10:15.772603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510141
 
0.4%
183135
 
0.4%
335117
 
0.3%
92117
 
0.3%
571115
 
0.3%
298114
 
0.3%
334112
 
0.3%
378108
 
0.3%
357107
 
0.3%
548105
 
0.3%
Other values (1787)36136
96.9%
ValueCountFrequency (%)
113
 
< 0.1%
74
 
< 0.1%
912
 
< 0.1%
1119
 
0.1%
124
 
< 0.1%
1315
 
< 0.1%
142
 
< 0.1%
1563
0.2%
1626
0.1%
1719
 
0.1%
ValueCountFrequency (%)
52608
< 0.1%
519417
< 0.1%
50896
 
< 0.1%
50483
 
< 0.1%
50473
 
< 0.1%
50211
 
< 0.1%
49993
 
< 0.1%
499813
< 0.1%
49771
 
< 0.1%
496512
< 0.1%

Ratio_View_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct30559
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.37744249
Minimum3.382
Maximum30172.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:15.892351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.382
5-th percentile11.2926
Q122.2495
median36.072
Q367.312
95-th percentile174.4099
Maximum30172.871
Range30169.489
Interquartile range (IQR)45.0625

Descriptive statistics

Standard deviation437.8933185
Coefficient of variation (CV)6.050135283
Kurtosis3470.021292
Mean72.37744249
Median Absolute Deviation (MAD)17.688
Skewness54.7018187
Sum2700185.247
Variance191750.5584
MonotonicityNot monotonic
2021-09-02T16:10:16.007536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.6056
 
< 0.1%
32.9996
 
< 0.1%
27.9725
 
< 0.1%
34.2565
 
< 0.1%
22.3725
 
< 0.1%
27.3335
 
< 0.1%
28.9215
 
< 0.1%
13.7875
 
< 0.1%
16.6345
 
< 0.1%
15.8885
 
< 0.1%
Other values (30549)37255
99.9%
ValueCountFrequency (%)
3.3821
< 0.1%
3.5611
< 0.1%
3.7631
< 0.1%
3.9421
< 0.1%
4.0131
< 0.1%
4.0922
< 0.1%
4.2711
< 0.1%
4.3011
< 0.1%
4.3391
< 0.1%
4.3861
< 0.1%
ValueCountFrequency (%)
30172.8711
< 0.1%
30156.1671
< 0.1%
29599.4951
< 0.1%
28917.0221
< 0.1%
27494.9181
< 0.1%
27447.7411
< 0.1%
26532.8781
< 0.1%
9825.5991
< 0.1%
9241.0061
< 0.1%
7919.3031
< 0.1%

Ratio_View_dislikes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct37036
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1740.412388
Minimum4.639
Maximum79800.948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:16.132244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.639
5-th percentile221.4069
Q1684.4115
median1285.613
Q32140.7735
95-th percentile4219.4086
Maximum79800.948
Range79796.309
Interquartile range (IQR)1456.362

Descriptive statistics

Standard deviation2447.818382
Coefficient of variation (CV)1.406458836
Kurtosis239.2430401
Mean1740.412388
Median Absolute Deviation (MAD)674.923
Skewness11.9994778
Sum64929564.97
Variance5991814.832
MonotonicityNot monotonic
2021-09-02T16:10:16.249317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
664.3095
 
< 0.1%
5.2223
 
< 0.1%
988.1053
 
< 0.1%
1163.4773
 
< 0.1%
664.333
 
< 0.1%
434.8072
 
< 0.1%
3742.3462
 
< 0.1%
1768.8762
 
< 0.1%
2137.9112
 
< 0.1%
1999.3632
 
< 0.1%
Other values (37026)37280
99.9%
ValueCountFrequency (%)
4.6391
 
< 0.1%
5.0571
 
< 0.1%
5.0921
 
< 0.1%
5.1161
 
< 0.1%
5.2151
 
< 0.1%
5.2211
 
< 0.1%
5.2223
< 0.1%
5.2231
 
< 0.1%
5.2251
 
< 0.1%
5.2261
 
< 0.1%
ValueCountFrequency (%)
79800.9481
< 0.1%
75753.4321
< 0.1%
63027.4771
< 0.1%
61286.0441
< 0.1%
61216.0971
< 0.1%
60796.141
< 0.1%
60588.0871
< 0.1%
60365.0961
< 0.1%
60169.4941
< 0.1%
60162.0391
< 0.1%

Ratio_views_comment_count
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct36607
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean949.772993
Minimum13.681
Maximum1421762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:16.373295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum13.681
5-th percentile70.3703
Q1198.9505
median381.577
Q3749.4485
95-th percentile2289.5125
Maximum1421762
Range1421748.319
Interquartile range (IQR)550.498

Descriptive statistics

Standard deviation12996.15484
Coefficient of variation (CV)13.68343271
Kurtosis6376.633545
Mean949.772993
Median Absolute Deviation (MAD)228.008
Skewness74.25807043
Sum35433181.05
Variance168900040.5
MonotonicityNot monotonic
2021-09-02T16:10:16.491465image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
525.323
 
< 0.1%
311.1013
 
< 0.1%
189.3793
 
< 0.1%
309.2033
 
< 0.1%
139.4923
 
< 0.1%
79.1793
 
< 0.1%
57.4493
 
< 0.1%
315.1913
 
< 0.1%
130.5963
 
< 0.1%
148.6993
 
< 0.1%
Other values (36597)37277
99.9%
ValueCountFrequency (%)
13.6811
< 0.1%
13.741
< 0.1%
16.6141
< 0.1%
17.0211
< 0.1%
17.3241
< 0.1%
17.3581
< 0.1%
17.4471
< 0.1%
17.6721
< 0.1%
17.8191
< 0.1%
18.1181
< 0.1%
ValueCountFrequency (%)
14217621
< 0.1%
961489.6671
< 0.1%
9340821
< 0.1%
9303511
< 0.1%
6189911
< 0.1%
522111.61
< 0.1%
464294.8331
< 0.1%
459688.51
< 0.1%
388770.3331
< 0.1%
387752.1431
< 0.1%

Ratio_likes_dislikes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30257
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.45089147
Minimum0.031
Maximum940.669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.6 KiB
2021-09-02T16:10:16.615284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.031
5-th percentile3.8492
Q116.063
median31.769
Q360.696
95-th percentile155.9518
Maximum940.669
Range940.638
Interquartile range (IQR)44.633

Descriptive statistics

Standard deviation58.40618173
Coefficient of variation (CV)1.181094617
Kurtosis24.08165957
Mean49.45089147
Median Absolute Deviation (MAD)19.509
Skewness3.792201279
Sum1844864.408
Variance3411.282064
MonotonicityNot monotonic
2021-09-02T16:10:16.729866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.515
 
< 0.1%
0.43815
 
< 0.1%
0.04110
 
< 0.1%
6.2269
 
< 0.1%
3.1359
 
< 0.1%
269
 
< 0.1%
548
 
< 0.1%
4.1238
 
< 0.1%
7.0837
 
< 0.1%
6.427
 
< 0.1%
Other values (30247)37210
99.7%
ValueCountFrequency (%)
0.0311
 
< 0.1%
0.0321
 
< 0.1%
0.0331
 
< 0.1%
0.0342
 
< 0.1%
0.0353
 
< 0.1%
0.0363
 
< 0.1%
0.0391
 
< 0.1%
0.04110
< 0.1%
0.0427
< 0.1%
0.0431
 
< 0.1%
ValueCountFrequency (%)
940.6691
< 0.1%
935.9581
< 0.1%
925.5441
< 0.1%
840.7511
< 0.1%
7601
< 0.1%
692.3021
< 0.1%
691.1671
< 0.1%
676.7971
< 0.1%
651.2741
< 0.1%
642.81
< 0.1%

Interactions

2021-09-02T16:09:56.683485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:56.820250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:56.931316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.042138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.155407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.276018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.393261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.506848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.620529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.740101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.864440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:57.986949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:58.100221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:58.301619image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:58.405378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:58.500160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:58.596957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:58.703161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:58.804764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:58.901112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.000328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.102222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.209015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.315260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.410849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.519250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.615687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.711714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.810345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:09:59.917542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.023482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.120631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.222501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.325908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.434679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.544905image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.641829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.754415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.854457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:00.953267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:01.054439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:01.165258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:01.274037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:01.469264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:01.581700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:01.690987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:01.804008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:01.914796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.015381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.136190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.245320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.354377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.462135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.583055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.700306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.808514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:02.924424image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.040181image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.170398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.292839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.401582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.520625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.626393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.731881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.841676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:03.958068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.070506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.177174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.284166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.396127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.512030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.626043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.732351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.841227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:04.937560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.034413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.132545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.238821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.470776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.579671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.680495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.782314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.890364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:05.995698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.092303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.204531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.304284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.405162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.507543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.617489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.731384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.831743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:06.934572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.040267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.153400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.262569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.364774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.478945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.580833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.686189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.791389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:07.906463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.018023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.121289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.226136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.334788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.452019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.569916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.675870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.799786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:08.911723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.023585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.138351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.259332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.375425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.490708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.606231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.724715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.849675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:09.973097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:10.086458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:10.357694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:10.480333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:10.590956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:10.703445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:10.823156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:10.939805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.050769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.163437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.278699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.400070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.521782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.633299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.740384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.836069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:11.933793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:12.033562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:12.140810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:12.242241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:12.337156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:12.437670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:12.542451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:12.650407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-09-02T16:10:12.757975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-09-02T16:10:16.839874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-02T16:10:17.029884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-02T16:10:17.216106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-02T16:10:17.404740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-02T16:10:12.956849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-02T16:10:13.231064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcategory_idviewslikesdislikescomment_countpublish_time_1publish_weekdaytitle_lengthdescription_lengthRatio_View_likesRatio_View_dislikesRatio_views_comment_countRatio_likes_dislikes
002672245155568110247947907:38:29Friday45821129.748705.037762.1605.434
11241053632255612294275706:24:44Sunday4141741.220459.299382.16611.143
2210171585797874204342012588217:00:03Friday4259421.791395.177136.30718.135
331727833193123702:30:38Monday76396144.2122319.417752.24316.083
442598153023001:45:13Monday55151327.1674907.500327.16715.000
55241182775527081431233317:00:00Saturday2881922.440826.537506.97636.833
6610335236221634124210828506711:04:14Thursday43125020.5151590.154394.08577.513
772211642015730974962419:19:43Friday2976320.3141554.3401865.70776.514
8810154494216314721108:00:01Friday4843471.4261050.980732.19914.714
99109548677190084150151147315:00:00Friday6069050.234635.943832.27412.660

Last rows

df_indexcategory_idviewslikesdislikescomment_countpublish_time_1publish_weekdaytitle_lengthdescription_lengthRatio_View_likesRatio_View_dislikesRatio_views_comment_countRatio_likes_dislikes
3729738906103839019797661662563917:00:01Thursday22153348.1292309.879680.79847.994
3729838907107608552930966025422204:00:00Thursday5269781.7281262.8301802.12015.452
372993890824266597526126599337716:00:03Wednesday59343102.0434450.710789.45143.616
3730038909106078793753352106126916:27:39Wednesday5146580.6902886.4164790.22335.772
37301389101019394001695781202588916:00:09Thursday42134111.4371613.478329.326141.080
3730238911102506695226808812783993307:00:01Wednesday6189093.5031960.9602523.60320.972
373033891210149221961998137812433017:09:16Friday5147524.069108.28161.3324.499
3730438913102964141239483088921998811:05:08Tuesday3432175.0743333.4921482.96044.403
37305389142414317515151870458752676620:32:32Tuesday7519594.275312.098534.9143.311
37306389151060755218271274142304:06:35Friday5150033.2522217.343426.95266.682